cognitive radio networks (chapters)

8/2/2019 Cognitive Radio Networks (Chapters)

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1. Introduction

Recent technological advances have resulted in the development of wireless

ad hoc networks composed of devices that are self-organizing and can bedeployed without infrastructure support. These devices generally have small form

factors, and have embedded storage, processing and communication ability.

While ad hoc networks may support different wireless standards, the current

state-of the- art has been mostly limited to their operations in the 900 MHz and

the 2.4 GHz industrial, scientific and medical (ISM) bands. With the growing

proliferation of wireless devices, these bands are increasingly getting congested.

At the same time, there are several frequency bands licensed to operators, suchas in the 400–700 MHz range, that are used sporadically or under-utilized for

transmission.

The licensing of the wireless spectrum is currently undertaken on a long-

term basis over vast geographical regions. In order to address the critical problem

of spectrum scarcity, the FCC has recently approved the use of unlicensed

devices in licensed bands. Consequently, dynamic spectrum access (DSA)

techniques are proposed to solve these current spectrum inefficiency problems. This new area of research foresees the development of cognitive radio (CR)

networks to further improve spectrum efficiency. The basic idea of CR networks is

that the unlicensed devices (also called cognitive radio users or secondary users)

need to vacate the band once the licensed device (also known as a primary user)

is detected. CR networks, however, impose unique challenges due to the high

fluctuation in the available spectrum as well as diverse quality of- service (QoS)

requirements. Specifically, in CR ad - hoc networks (CRAHNs), the distributedmulti-hop architecture, the dynamic network topology, and the time and location

varying spectrum availability are some of the key distinguishing factors. These

challenges necessitate novel design techniques that simultaneously address a

wide range of communication problems spanning several layers of the protocol

stack.

Cognitive radio technology is the key technology that enables a CRAHN to

use spectrum in a dynamic manner. The term, cognitive radio, can formally bedefined as follows:



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A ‘‘Cognitive Radio” is a radio that can change its transmitter parameters based

on interaction with the environment in which it operates. From this definition,

two main characteristics of the cognitive radio can be defined as follows:

• Cognitive capability: Cognitive capability refers to the ability of the radio

technology to capture or sense the information from its radio environment.

This capability cannot simply be realized by monitoring the power in some

frequency bands of interest but more sophisticated techniques, such as

autonomous learning and action decision are required in order to capture

the temporal and spatial variations in the radio environment and avoid

interference to other users. Through this capability, the portions of the

spectrum that are unused at a specific time or location can be identified.

Consequently, the best spectrum and appropriate operating parameters

can be selected.

• Reconfigurability: The cognitive capability provides spectrum awareness

whereas Reconfigurability enables the radio to be dynamically programmed

according to the radio environment. More specifically, the cognitive radio

can be programmed to transmit and receive on a variety of frequencies and

to use different transmission access technologies supported by its

hardware design.

Cognitive Radio Features

The idea of a cognitive radio extends the concepts of a hardware radio and

a software defined radio (SDR) from a simple, single function device to a radio

that senses and reacts to its operating environment.

A Cognitive Radio incorporates multiple sources of information, determines

its current operating settings, and collaborates with other cognitive radios in a

wireless network. The promise of cognitive radios is improved use of spectrum

resources, reduced engineering and planning time, and adaptation to current

operating conditions.

Some features of cognitive radios include:

• Sensing the current radio frequency spectrum environment: This includes

measuring which frequencies are being used, when they are used,

estimating the location of transmitters and receivers, and determining



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signal modulation. Results from sensing the environment would be used to

determine radio settings.

• Policy and configuration databases : Policies specifying how the radio can be

operated and physical limitations of radio operation can be included in the

radio or accessed over the network. Policies might specify which

frequencies can be used in which locations. Configuration databases would

describe the operating characteristics of the physical radio. These

databases would normally be used to constrain the operation of the radio

to stay within regulatory or physical limits.

• Self-configuration : Radios may be assembled from several modules. For

example, a radio frequency front-end, a digital signal processor, and a

control processor. Each module should be self-describing and the radio

should automatically configure itself for operation from the available

modules. Some might call this “plug-and-play.”

• Mission-oriented configuration : Software defined radios can meet a wide set

of operational requirements. Configuring a SDR to meet a given set of

mission requirements is called mission oriented configuration. Typical

mission requirements might include operation within buildings, substantial

capacity, operation over long distances, and operation while moving at high

speed. Mission-oriented configuration involves selecting a set of radio

software modules from a library of modules and connecting them into an

operational radio.

• Adaptive algorithms : During radio operation, the cognitive radio is sensing

its environment, adhering to policy and configuration constraints, and

negotiating with peers to best utilize the radio spectrum and meet user

demands.

• Distributed collaboration : Cognitive radios will exchange current

information on their local environment, user demand, and radio

performance between themselves on a regular base. Radios will use their

local information and peer information to determine their operating

settings.

• Security : Radios will join and leave wireless networks.



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Defining “Cognitive Radio”

Tautologically, a cognitive radio could be defined as “A radio that is

cognitive,”

In the 1999 paper that first coined the term “cognitive radio”, Joseph

Mitola III defines a cognitive radio as [Mitola_99]: “A radio that employs model

based reasoning to achieve a specified level of competence in radio-related

domains .”

However, in his recent popularly cited paper that surveyed the state of

cognitive radio, Simon Haykin defines a cognitive radio as [Haykin_05]: “An

intelligent wireless communication system that is aware of its surrounding

environment (i.e., outside world), and uses the methodology of understanding-by-

building to learn from the environment and adapt its internal states to statistical

variations in the incoming RF stimuli by making corresponding changes in certain

operating parameters (e.g., transmit-power, carrier frequency, and modulation

strategy) in real-time, with two primary objectives in mind:

• highly reliable communications whenever and wherever needed

• efficient utilization of the radio spectrum.

• It thinks, therefore it’s a cognitive radio

“An adaptive radio that is capable of the following:

a) Awareness of its environment and its own capabilities,

b) Goal driven autonomous operation,

c) Understanding or learning how its actions impact its goal,

d) Recalling and correlating past actions, environments, and performance.”

Finally, the following are some general capabilities found in all of the definitions:

1. Observation – whether directly or indirectly, the radio is capable of acquiring

information about its operating environment.

2. Adaptibility – the radio is capable of changing its waveform.

3. Intelligence – the radio is capable of applying information towards a purposeful

goal.



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2. Classical ad hoc networks vs. cognitive radio ad hoc

networks

The changing spectrum environment and the importance of protecting the

transmission of the licensed users of the spectrum mainly differentiate classical

ad hoc networks from CRAHNs. We describe these unique features of CRAHNs

compared to classical ad hoc networks as follows:

Choice of transmission spectrum: In CRAHNs, the available spectrum bands are

distributed over a wide frequency range, which vary over time and space. Thus,

each user shows different spectrum availability according to the primary user

(PU) activity. As opposed to this, classical ad hoc networks generally operate on a

pre-decided channel that remains unchanged with time. For the ad hoc networks

with multi-channel support, all the channels are continuously available for

transmission, though nodes may select few of the latter from this set based on

self-interference constraints. A key distinguishing factor is the primary

consideration of protecting the PU transmission, which is entirely missing in

classical ad hoc networks.

(a) (b)

Fig. 2. Comparison between CR capabilities for: (a) infrastructure-based CR networks, and (b)

CRAHNs

Since the CR user cannot predict the influence of its actions on the entire

network with its local observation, cooperation schemes are essential, where the

observed information can be exchanged among devices to broaden the knowledge

on the network.

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3. Spectrum management framework for cognitive radio

ad hoc networks

The components of the cognitive radio ad hoc network (CRAHN)

architecture, as shown in Fig. 3a, can be classified in two groups as the primary

network and the CR network components. The primary network is referred to as

an existing network, where the primary users (PUs) have a license to operate in a

certain spectrum band. If primary networks have an infrastructure support, the

operations of the Pus are controlled through primary base stations. Due to their

priority in spectrum access, the PUs should not be affected by unlicensed users.

The CR network (or secondary network) does not have a license to operate in a

desired band. Hence, additional functionality is required for CR users (or

secondary user)1 to share the licensed spectrum band. Also, CR users are mobile

and can communicate with each other in a multi-hop manner on both licensed

and unlicensed spectrum bands. Usually, CR networks are assumed to function

as stand-alone networks, which do not have direct communication channels with

the primary networks. Thus, every action in CR networks depends on their local

observations.

In the cognition cycle, a radio receives information about its operating

environment (Outside world) through direct observation or through signalling.

This information is then evaluated (Orient ) to determine its importance. Based on

this valuation, the radio determines its alternatives (Plan) and chooses an

alternative (Decide) in a way that presumably would improve the valuation.

Assuming a waveform change was deemed necessary, the radio then implements

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the alternative ( Act ) by adjusting its resources and performing the appropriate

signalling. These changes are then reflected in the interference profile presented

by the cognitive radio in the Outside world. As part of this process, the radio uses

these observations and decisions to improve the operation of the radio (Learn),

perhaps by creating new modelling states, generating new alternatives, or

creating new valuations.

Figure: Cognition cycle

In order to adapt to dynamic spectrum environment, the CRAHN

necessitates the spectrum-aware operations, which form a cognitive cycle. As

shown in Fig. 3b, the steps of the cognitive cycle consist of four spectrum

management functions: spectrum sensing, spectrum decision, spectrum sharing,

and spectrum mobility. To implement CRAHNs, each function needs to be

incorporated into the classical layering protocols, as shown in Fig. 4. The

following are the main features of spectrum management functions:

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• Spectrum sensing: A CR user can be allocated to only an unused portion of

the spectrum. Therefore, a CR user should monitor the available spectrum

bands, and then detect spectrum holes. Spectrum sensing is a basic

functionality in CR networks, and hence it is closely related to other

spectrum management functions as well as layering protocols to provide

information on spectrum availability.

• Spectrum decision: Once the available spectrums are identified, it is

essential that the CR users select the most appropriate band according to

their QoS requirements. It is important to characterize the spectrum band

in terms of both radio environment and the statistical behaviours of the

PUs. In order to design a decision algorithm that incorporates dynamic

spectrum characteristics, we need to obtain a priori information regarding

the PU activity. Furthermore, in CRAHNs, spectrum decision involves

jointly undertaking spectrum selection and route formation.

• Spectrum sharing: Since there may be multiple CR users trying to access the

spectrum, their transmissions should be coordinated to prevent collisions

in overlapping portions of the spectrum. Spectrum sharing provides the

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capability to share the spectrum resource opportunistically with multiple

CR users which includes resource allocation to avoid interference caused to

the primary network. For this, game theoretical approaches have also been

used to analyze the behaviour of selfish CR users. Furthermore, this

function necessitates a CR medium access control (MAC) protocol, which

facilitates the sensing control to distribute the sensing task among the

coordinating nodes as well as spectrum access to determine the timing for

transmission.

• Spectrum mobility: If a PU is detected in the specific portion of the spectrum

in use, CR users should vacate the spectrum immediately and continue

their communications in another vacant portion of the spectrum. For this,

either a new spectrum must be chosen or the affected links may be

circumvented entirely. Thus, spectrum mobility necessitates a spectrum

handoff scheme to detect the link failure and to switch the current

transmission to a new route or a new spectrum band with minimum

quality degradation. This requires collaborating with spectrum sensing,

neighbour discovery in a link layer, and routing protocols. Furthermore,

this functionality needs a connection management scheme to sustain the

performance of upper layer protocols by mitigating the influence of

spectrum switching.

To overcome the drawback caused by the limited knowledge of the

network, all of spectrum management functions are based on cooperative

operations where CR users determine their actions based on the observed

information exchanged with their neighbours.



4. Spectrum sensing for cognitive radio ad hoc networks

A cognitive radio is designed to be aware of and sensitive to the changes in

its surrounding, which makes spectrum sensing an important requirement for

the realization of CR networks. Spectrum sensing enables CR users to exploit the

unused spectrum portion adaptively to the radio environment. This capability isrequired in the following cases:

(1) CR users find available spectrum holes over a wide frequency range for

their transmission (out-of-band sensing), and

(2) CR users monitor the spectrum band during the transmission and detect

the presence of primary networks so as to avoid interference (inband

sensing).

As shown in Fig. 5, the CRAHN necessitates the following functionalities for

spectrum sensing:

• PU detection: The CR user observes and analyzes its local radio

environment. Based on these location observations of itself and its

neighbours, CR users determine the presence of PU transmissions, and

accordingly identify the current spectrum availability.

•

Cooperation: The observed information in each CR user is exchanged withits neighbours so as to improve sensing accuracy.

• Sensing control: This function enables each CR user to perform its sensing

operations adaptively to the dynamic radio environment. In addition, it

coordinates the sensing operations of the CR users and its neighbours in a

distributed manner, which prevents false alarms in cooperative sensing.

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In order to achieve high spectrum utilization while avoiding interference,

spectrum sensing needs to provide high detection accuracy. However, due to the

lack of a central network entity, CR ad hoc users perform sensing operations

independently of each other, leading to an adverse influence on sensing

performance. In the following subsection, we investigate these basic

functionalities required for spectrum sensing to address this challenge in

CRAHNs.

4.1. Primary user detection

Since CR users are generally assumed not to have any real-time interaction

with the PU transmitters and receivers, they do not know the exact information of

the ongoing transmissions within the primary networks. Thus, PU detection

depends on the only local radio observations of CR users. Generally, PU detection

techniques for CRAHNs can be classified into three groups: primary transmitter

detection, primary receiver detection, and interference temperature management

(see Fig. 6).

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As shown in Fig. 7a, transmitter detection is based on the detection of the weak

signal from a primary transmitter through the local observations of CR users. The

primary receiver detection aims at finding the PUs that are receiving data within

the communication range of a CR user [86]. As depicted in Fig. 7b, the local

oscillator (LO) leakage power emitted by the radio frequency (RF) front-end of the



primary receiver is usually exploited, which is typically weak. Thus, although it

provides the most effective way to find spectrum holes, currently this method is

only feasible in the detection of the TV receivers. Interference temperature

management accounts for the cumulative RF energy from multiple transmissions,

and sets a maximum cap on their aggregate level that the primary receiver could

tolerate, called an interference temperature limit.

As long as CR users do not exceed this limit by their transmissions, they

can use this spectrum band. However, the difficulty of this model lies in

accurately measuring the interference temperature since CR users cannot

distinguish between actual signals from the PU and noise/interference. For these

reasons, most of current research on spectrum sensing in CRAHNs has mainly

focused on primary transmitter detection.

4.1.1. Matched filter detection

The matched filter is the linear optimal filter used for coherent signal

detection to maximize the signal-to-noise ratio (SNR) in the presence of additive

stochastic noise. As shown in Fig. 8, it is obtained by correlating a known original

PU signal s(t) with a received signal r(t) where T is the symbol duration of PU

signals. Then the output of the matched filter is sampled at the synchronized

timing. If the sampled value Y is greater than the threshold k, the spectrum is

determined to be occupied by the PU transmission. This detection method is

known as an optimal detector in stationary Gaussian noise. It shows a fast

sensing time, which requires O(1/SNR) samples to achieve a given target

detection probability. However, the matched filter necessitates not only a priori

knowledge of the characteristics of the PU signal but also the synchronization

between the PU transmitter and the CR user. If this information is not accurate,

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then the matched filter performs poorly. Furthermore, CR users need to have

different multiple matched filters dedicated to each type of the PU signal, which

increases the implementation cost and complexity. For more practical

implementation, a pilot signal of PU systems is used for the matched filter

detection in. In this method, PU transmitters send the pilot signal simultaneously

with data, and CR users have its perfect knowledge, which may not still feasible

in CRAHNs. For this reason, energy detection and feature detection are the most

commonly used for spectrum sensing in CRAHNs.

4.1.2. Energy detection

The energy detector is optimal to detect the unknown signal if the noise

power is known. In the energy detection, CR users sense the presence/absence of

the PUs based on the energy of the received signals. As shown in Fig. 9, the

measured signal r(t) is squared and integrated over the observation interval T.

Finally, the output of the integrator is compared with a threshold k to decide if a

PU is present.

While the energy detector is easy to implement, it has several shortcomings. The

energy detector requires O(1/ SNR2) samples for a given detection probability.

Thus, if CR users need to detect weak PU signals (SNR: _10 dB to _40 dB), the

energy detection suffers from longer detection time compared to the matched

filter detection. Furthermore, since the energy detection depends only on the SNR

of the received signal, its performance is susceptible to uncertainty in noise

power. If the noise power is uncertain, the energy detector will not be able to

detect the signal reliably as the SNR is less than a certain threshold, called an

SNR wall. In addition, while the energy detector can only determine the presence

of the signal but cannot differentiate signal types. Thus, the energy detector often

results in false detection triggered by the unintended CR signals. For these

reasons, in order to use energy detection, CRAHNs need to provide the

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synchronization over the sensing operations of all neighbours, i.e., each CR user

should be synchronized with the same sensing and transmission schedules.

Otherwise, CR users cannot distinguish the received signals from primary and

CR users, and hence the sensing operations of the CR user will be interfered by

the transmissions of its neighbours.

4.1.3. Feature detection

Feature detection determines the presence of PU signals by extracting their

specific features such as pilot signals, cyclic pre.xes, symbol rate, spreading

codes, or modulation types from its local observation. These features introduce

built-in periodicity in the modulated signals, which can be detected by analyzing

a spectral correlation function as shown in Fig. 10. The feature detection

leveraging this periodicity is also called cyclostationary detection. Here, the

spectrum correlation of the received signal r(t) is averaged over the interval T, and

compared with the test statistic to determine the presence of PU signals, similar

to energy detection . The main advantage of the feature detection is its

robustness to the uncertainty in noise power. Furthermore, it can distinguish the

signals from different networks. This method allows the CR user to perform

sensing operations independently of those of its neighbours without

synchronization. Although feature detection is most effective for the nature of

CRAHNs, it is computationally complex and requires significantly long sensing

time. In the enhanced feature detection scheme combining cyclic spectral

analysis with pattern recognition based on neural networks is proposed. The

distinct features of the received signal are extracted using cyclic spectral analysis

and represented by both spectral coherent function and spectral correlation

density function. The neural network, then, classi.es signals into different

modulation types. In it is shown that the feature detection enables the detection

of the presence of the Gaussian minimum shift keying (GMSK)modulated GSM

signal (PU signal) in the channel under severe interference from the orthogonal

frequency division multiplexing (OFDM) based wireless LAN signal (CR signal) by

exploiting different cyclic signatures of both signals. A covariance-based detection

scheme based on the statistical covariance or autocorrelations of the received

signal is proposed in. The statistical covariance matrices or autocorrelations of

signal and noise are generally different. The statistical covariance matrix of noise

is determined by the receiving filter. Based on this characteristic, it differentiates



the presence of PU users and noise. The method can be used for various signal

detection applications without knowledge of the signal, the channel and noise

power.

4.2. Sensing control

The main objective of spectrum sensing is to find more spectrum access

opportunities without interfering with primary networks. To this end, the sensing

operations of CR users are controlled and coordinated by a sensing controller,

which considers two main issues on:

(1) How long and frequently CR users should sense the spectrum to achieve

sufficient sensing accuracy in in-band sensing, and

(2) How quickly CR user can find the available spectrum band in out-of-band

sensing, which are summarized in Fig. 11.

4.2.1. In‐band sensing control

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The first issue is related to the maximum spectrum opportunity as well as

interference avoidance. The in-band sensing generally adopts the periodic sensing

structure where CR users are allowed to access the spectrum only during the



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transmission period followed by sensing (observation) period. In the periodic

sensing, longer sensing time leads to higher sensing accuracy, and hence to less

interference. But as the sensing time becomes longer, the transmission time of

CR users will be decreased. Conversely, while longer transmission time increases

the access opportunities, it causes higher interference due to the lack of sensing

information. Thus, how to select the proper sensing and transmission times is an

important issue in spectrum sensing. The sensing time is determined to

maximize the channel efficiency while maintaining the required detection

probability, which does not consider the influence of a false alarm probability.

T he sensing time is optimized for a multiple spectrum environment so as to

maximize the throughput of CR users.

4.2.2. Out ‐of ‐band sensing control

When a CR user needs to find new available spectrum band (out-of-band

sensing), a spectrum discovery time is another crucial factor to determine the

performance of CRAHNs. Thus, this spectrum sensing should have coordination

scheme not only to discover as many spectrum opportunities as possible but also

to minimize the delay in finding them. This is also an important issue in

spectrum mobility to reduce the switching time. First, the proper selection of

spectrum sensing order can help to reduce the spectrum discovery time in out-of-

band sensing.

4.3 Cooperation

In CRAHNs, each CR user needs to determine spectrum availability by itself

depending only on its local observations. However the observation range of the

CR user is small and typically less than its transmission range. Thus, even

though CR users find the unused spectrum portion, their transmission may

cause interference at the primary receivers inside their transmission range, the

so-called receiver uncertainty problem.

Furthermore, if the CR user receives a weak signal with a low signal-

tonoise ratio (SNR) due to multi-path fading, or it is located in a shadowing area,

it cannot detect the signal of the PUs. Thus, in CRAHNs, spectrum sensing

necessitates an efficient cooperation scheme in order to prevent interference to

PUs outside the observation range of each CR user .A common cooperative

scheme is forming clusters to share the sensing information locally. For



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cooperation, when a CR user detects the PU activities, it should notify its

observations promptly to its neighbours to evacuate the busy spectrum. To this

end, a reliable control channel is needed for discovering neighbours of a CR user

as well as exchanging sensing information. In addition to this, asynchronous

sensing and transmission schedules make it difficult to exchange sensing

information between neighbours.

Thus, robust neighbour discovery and reliable information exchange are

critical issues in implementing cooperative sensing in CRAHNs. This cooperation

issue will be also leveraged by other spectrum management functions: spectrum

decision, spectrum sharing, and spectrum mobility. Cooperative detection is

theoretically more accurate since the uncertainty in a single user detection can

be minimized through collaboration. Moreover, multipath fading and shadowing

effects can be mitigated so that the detection probability is improved in a heavily

shadowed environment. However, cooperative approaches because adverse effects

on resource constrained networks due to the overhead traffic.



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5. Spectrum decision for cognitive radio ad hoc networks

CRAHNs require capabilities to decide on the best spectrum band among

the available bands according to the QoS requirements of the applications. This

notion is called spectrum decision and constitutes a rather important but yet

unexplored topic.

Spectrum decision is closely related to the channel characteristics and the

operations of PUs. Spectrum decision usually consists of two steps: First, each

spectrum band is characterized based on not only local observations of CR users

but also statistical information of primary networks. Then, based on this

characterization, the most appropriate spectrum band can be chosen.

Generally, CRAHNs have unique characteristics in spectrum decision dueto the nature of multi-hop communication. Spectrum decision needs to consider

the end-to-end route consisting of multiple hops. Furthermore, available

spectrum bands in CR networks differ from one hop to the other. As a result, the

connectivity is spectrum-dependent, which makes it challenging to determine the

best combination of the routing path and spectrum. Thus, spectrum decision in

ad hoc networks should interact with routing protocols.

The following are main functionalities required for spectrum decision:

• Spectrum characterization: Based on the observation, the CR users

determine not only the characteristics of each available spectrum but also

its PU activity model.

• Spectrum selection: The CR user finds the best spectrum band for each hop

on the determined end-to-end route so as to satisfy end-to-end QoSrequirements.

• Reconfiguration: The CR users reconfigure communication protocol as well

as communication hardware and RF front-end according to the radio

environment and user QoS requirements.

CR ad hoc users require spectrum decision in the beginning of the transmission.

CR users characterize the available spectrum bands by considering the receivedsignal strength, interference, and the number of users currently residing in the

spectrum, which are also used for resource allocation in classical ad hoc



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networks. However, unlike classical ad hoc networks, each CR user observes

heterogeneous spectrum availability which is varying over time and space due to

the PU activities. This changing nature of the spectrum usage is considered in

the spectrum characterization. Based on this characterization, CR users

determine the best available spectrum band to satisfy its QoS requirements.

Furthermore, quality degradation of the current transmission can also initiate

spectrum decision to maintain the quality of a current session.

5.1. Spectrum characterization

5.1.1. Radio environment

Since the available spectrum holes show different characteristics, which

vary over time, each spectrum hole should be characterized by considering both

the time varying radio environment and the spectrum parameters such as

operating frequency and bandwidth. Hence, it is essential to define parameters

that can represent a particular spectrum band as follows:

• Interference: From the amount of the interference at the primary receiver,

the permissible power of a CR user can be derived, which is used for the

estimation of the channel capacity.

• Path loss: The path loss is closely related to the distance and frequency. As

the operating frequency increases, the path loss increases, which results in

a decrease in the transmission range. If transmission power is increased to

compensate for the increased path loss, interference at other users may

increase.

• Wireless link errors: Depending on the modulation scheme and the

interference level of the spectrum band, the error rate of the channel

changes.

• Link layer delay: To address different path loss, wireless link error, and

interference, different types of link layer protocols are required at different

spectrum bands. This results in different link layer delays. It is desirable to

identify the spectrum bands combining all the characterization parameters

described above for accurate spectrum decision. However, a complete

analysis and modeling of spectrum in CR networks is yet to be developed.



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5.1.2. Primary user activity

In order to describe the dynamic nature of CR networks, we need a new

metric to capture the statistical behaviour of primary networks, called primary

user (PU) activity. Since there is no guarantee that a spectrum band will be

available during the entire communication of a CR user, the estimation of PU

activity is a very crucial issue in spectrum decision.

Most of CR research assumes that PU activity is modelled by exponentially

distributed inter-arrivals [4, 15, 44, 45, 49, and 93]. In this model, the PU traffic

can be modelled as a two state birth–death process with death rate and birth rate

5.2. Spectrum selection

Once the available spectrum bands are characterized, the most appropriate

spectrum band should be selected. Based on user QoS requirements and the

spectrum characteristics, the data rate, acceptable error rate, delay bound, the

transmission mode, and the bandwidth of the transmission can be determined.

Then, according to a spectrum selection rule, the set of appropriate spectrum

bands can be chosen. However, as stated previously, since the entire

communication session consists of multiple hops with heterogeneous spectrum

availability, the spectrum selection rule is closely coupled with routing protocols

in CRAHNs.

Since there exist numerous combinations of route and spectrum between

the source and destination, it is infeasible to consider all possible links for

spectrum decision. In order to determine the best route and spectrum more

efficiently, spectrum decision necessitates the dynamic decision framework to

adapt to the QoS requirements of the user and channel conditions. Furthermore,

in recent research, the route selection is performed independent of the spectrum

decision. Although this method is quite simple, it cannot provide an optimal

route because spectrum availability on each hop is not considered during route

establishment. Thus, joint spectrum and routing decision method is essential for

CRAHNs.

5.3. Reconfiguration

Besides spectrum and route selection, spectrum decision involves

reconfiguration in CRAHNs. The protocols for different layers of the network stack

must adapt to the channel parameters of the operating frequency. Once the



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spectrum is decided, CR users need to select the proper communication modules

such as physical layer technology and upper layer protocols adaptively dependent

on application requirements as well as spectrum characteristics, and then

reconfigure their communication system accordingly.

The adaptive protocols are developed to determine the transmission power

as well as the best combination of modulation and error correction code for a new

spectrum band by considering changes in the propagation loss.



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6. Spectrum sharing for cognitive radio ad hoc networks

The shared nature of the wireless channel necessitates coordination of

transmission attempts between CR users. In this respect, spectrum sharing

provides the capability to maintain the QoS of CR users without causinginterference to the PUs by coordinating the multiple access of CR users as well

as allocating communication resources adaptively to the changes of radio

environment.

Thus, spectrum sharing is performed in the middle of a communication

session and within the spectrum band, and includes much functionality of a

medium access control (MAC) protocol and resource allocation in classical ad hoc

networks. However, the unique characteristics of cognitive radios such as thecoexistence of CR users with PUs and the wide range of available spectrum incur

substantially different challenges for spectrum sharing in CRAHNs. Spectrum

sharing techniques are generally focused on two types of solutions, i.e., spectrum

sharing inside a CR network (intra-network spectrum sharing), and among

multiple coexisting CR networks (inter-network spectrum sharing).

However, since the CRAHNs do not have any infrastructure to coordinate

inter-network operations, they are required to consider the only intra-networkspectrum sharing functionality. Furthermore, similar to spectrum sensing, the

CR users need to have all CR sharing capabilities due to the lack of a central

entity. Thus, all decisions on spectrum sharing need to be made by CR users in a

distributed manner. Spectrum sharing shares some functionality with spectrum

sensing in CRAHNs as follows:

• Resource allocation: Based on the QoS monitoring results, CR users select

the proper channels (channel allocation) and adjust their transmissionpower (power control) so as to achieve QoS requirements as well as

resource fairness. Especially, in power control, sensing results need to be

considered so as not to violate the interference constraints.

• Spectrum access: It enables multiple CR users to share the spectrum

resource by determining who will access the channel or when a user may

access the channel.



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Medium access protocols for spectrum access

– Random access

• Channel may be opportunistically captured by any CR user for

control and data exchanges

– Time slotted

• Control and data are assigned fixed durations and prevent

simultaneous transmission by multiple users

– Hybrid

• Fixed time duration for control packets followed by random

access for capturing the channel before data transfer.



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7. Spectrum mobility for cognitive radio ad hoc networks

• CR users are mobile and so called visitors to the spectrum

• If the spectrum in use by a CR user is required for PU, the communication

of the CR user needs to be continued in another vacant portion of the

spectrum.

• Spectrum mobility is required when:

• PU is detected

• CR user loses its connection due to mobility of other users

• Current spectrum band cannot provide the QoS requirements.

Spectrum mobility is achieved through:

Spectrum handoff:

• CR user switches the spectrum band physically and reconfigures the

communication parameters (e.g. operating frequency, modulation type).

Connection management:

• CR user sustains the QoS or minimizes quality degradation during

spectrum switching by interacting with each layer.

7.1. Spectrum handoff

Spectrum handoff can be implemented based on two different strategies. In

reactive spectrum handoff, CR users perform spectrum switching after detecting

link failure due to spectrum mobility. This method requires immediate spectrum

switching without any preparation time, resulting in significant quality

degradation in on-going transmissions. On the other hand, in proactive spectrum

handoff CR users predict future activity in the current link and determine a new

spectrum while maintaining the current transmission, and then perform

spectrum switching before the link failure happens. Since proactive spectrum

handoff can maintain current transmissions while searching a new spectrum

band, the spectrum switching is faster but requires more complex algorithms for

these concurrent operations. Depending on the events that triggers the spectrum

mobility, different handoff strategies are needed. While reactive spectrum handoff

is generally used in the event of a PU appearance, proactive spectrum handoff is



26

suitable for the events of user mobility or spectrum quality degradation. These

events do not require immediate spectrum switching, and can be easily predicted.

Even in the PU appearance event, the proactive spectrum handoff may be used

instead of the reactive scheme, but requires an accurate model for PU activity to

avoid an adverse influence on communication performance.

In addition, for seamless communication in dynamic radio environments,

this spectrum handoff should support intelligent connection releasing and re-

establishing procedures during spectrum switching. When a CR user is moving, it

needs to determine whether it should stay connected to its next hop forwarder

through power control or immediately switching to a new neighbour. This has to

be undertaken ensuring the network stays connected throughout the handoff

procedure.

Spectrum handoff delay is the most crucial factor in determining the

performance of spectrum mobility. This delay is dependent on the following

operations in CR networks: First, the different layers of the protocol stack must

adapt to the channel parameters of the operating frequency. Thus, each time a

CR user changes its frequency, the network protocols may require modifications

on the operation parameters, which may cause protocol reconfiguration delay.

Also we need to consider the spectrum and route recovery time and the actual

switching time determined by the RF front-end reconfiguration. Furthermore, to

find the new spectrum and route, CR users need to perform out-of band sensing

and neighbour discovery. Recent research has explored the minimization of the

delay in out-of-band sensing through the search-sequence optimization.

Furthermore, for more efficient spectrum discovery in out-of-band sensing, IEEE

802.22 adopts the backup channel lists which are selected and maintained so as

to provide the highest probability of finding an available spectrum band within

the shortest time.

7.2. Connection management

When the current operational frequency becomes busy in the middle of a

communication by a CR user, then applications running in this node have to be

transferred to another available frequency band. However, the selection of new

operational frequency may take time. An important requirement of connection

management protocols is the information about the duration of a spectrum

handoff. Once the latency information is available, the CR user can predict the



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influence of the temporary disconnection on each protocol layer, and accordingly

preserve the ongoing communications with only minimum performance

degradation through the reconfiguration of each protocol layer and an error

control scheme. Consequently, multi-layer mobility management protocols are

required to accomplish the spectrum mobility functionalities. These protocols

support mobility management adaptive to different types of applications. For

example, a transmission control protocol (TCP) connection can be put to a wait

state until the spectrum handoff is over. Moreover, since the TCP parameters will

change after a spectrum handoff, it is essential to learn the new parameters and

ensure that the transitions from the old parameters to new parameters are

carried out rapidly.



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8. Antennas for CR systems

The key distinction is that the CR would perform intelligent decision

making. To inform these decisions the CR would continually scan the available

frequency spectrum. This information would be used in order to detect and

classify any legacy users who may begin transmitting on the frequency. In this

event protocol would probably dictate that the CR should vacate the band

immediately. During each frequency scan the CR would also identify unused

areas of spectrum together with radio born interference on its operating

frequency. This information would be used when reconfiguring the radio in order

to select an area of free spectrum which best suited the user needs.

Factors involved in this decision would include the content of message (e.g.

urgent or non-urgent), the bandwidth required, and the spot price for renting

spectrum. This flexible pooling of the radio spectrum would provide the user with

an improved and more reliable service. It would also liberalize the conditions for

spectrum trading and make far more intensive use of the entire radio spectrum

whilst avoiding spectrum traffic jams.

It is clear that the CR will require some rather specialized antennas and

front-end transceiver circuitry in order to handle the demands of frequency

scanning and communication. To illustrate typical challenges in antennas for CR

the existing FCC UWB band (3.1 GHz to 10.6 GHz) is chosen, although a fuller

implementation may involve frequencies from 400 MHz (or lower) to 10 GHz.

Although the architecture of CR has not yet been standardized, some

experts suggest that an ultra- wideband (UWB) antenna should be used for

performing the sensing function. A narrowband antenna with a reconfigurable

frequency would then handle communications. There is an extensive body of

literature on planar UWB antennas. Many of these antennas have evolved from

broadband 3-D structures which include the biconical antenna. There are

however no examples of very closely integrated wide and narrowband antennas in

the literature. This kind of integration would be essential within a portable CR

handset where the available space would be very limited. The antennas reported

here were specifically developed to address this need. Narrowband Shorted Patch

Antenna and Integrated Wine-Glass Shaped UWB Monopole are the two antennas

described here. The antenna is printed on a Taconic TLC substrate with a relative

permittivity of er=3+/-0.05 and a thickness ofh=0.79 mm.



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9. CR system applications:

9.1 Government and Regulatory Interest

With the promises of an intelligent and aware device, a wide range of

applications have emerged, from Dynamic Spectrum Access (DSA) to

interoperability solutions to the idea of a universal portable communicator; all of

these target markets that range from military to commercial. DSA is currently

being considered as the prime candidate for the first practical application of

cognitive radio technology. The impact and possible importance of this

application is felt throughout the United States agencies responsible for spectrum

management.

The Federal Communications Commission (FCC), the National

Telecommunications and Information Administration (NTIA) and the Department

of State have expressed interest in what CR technology has to offer and how it

would affect their current regulatory scheme. In particular, the FCC has

launched a set of initiatives to facilitate the development and deployment of this

technology. One of their most recent actions will allow the use of cognitive

radios/cognitive applications to be incorporated into certain TV bands.

The CR impact also extends beyond our geographical border as other

countries and international agencies such as the International

Telecommunications Union (ITU) are looking to adopt a similar cognitive radio

approach to increase spectrum utilization. We acknowledge that although

dynamic spectrum access looks to be very promising, the complexity required to

achieve it could be overwhelmingly difficult.

9.2 Military

The military community has recognized the benefits that this new radio

technology offers. With frequency agility and/or flexibility, the ability to enhance

interoperability between different radio standards, and the capability to sense the

presence of interferers, CR has become a must-have technology. This technology

offers advantages in the protection of communication transmissions, recognition

of enemy communications, and the discovery of paths of opportunity. By

recognizing other communication devices, the cognitive radio can address



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interoperability issues by adjusting itself to communicate with legacy systems.

The U.S.Department of Defense (DoD) has devoted a great amount of effort to

advanced wireless topics in recent years and has established programs such as

Speakeasy radio system, Joint Tactical Radio System (JTRS), and next

Generation (XG) to further explore the possibilities of the creation of an intelligent

communication agent.

9.3 Public Safety

Public safety and emergency response is another area in which cognitive

radio has gained a lot of attention. For years public safety agencies have

desperately needed additional spectrum allocation to ease frequency congestion

and enhance interoperability. With spectrum sharing capabilities, cognitive

radios can prove their effectiveness by utilizing some of the existing spectrum

that is not widely used while help in maintaining call priority and response time.

In addition, CRs can play an important part in improving interoperability by

enabling devices to bridge communications between jurisdictions using different

frequencies and modulation formats. The National Institute of Justice issued a

call for proposals in which they seek to find a technology that can not only

provide them with a solution to the interoperability issue they currently face but

with an ubiquitous system able to handle communication needs yet to

come.

9.4 Broader Impacts and Commercial Use

With the proliferation of wireless technologies in the ISM band, especially

after the success of wireless local area networks (WLAN) like 802.11, interference

is becoming increasingly problematic. In urban environments, the ISM band is

already showing the symptoms of spectrum scarcity as the demand for its use

continues to increase and performance degradation becomes the norm. Although

some technologies are currently using some adaptive techniques (802.11g uses

channel identification, dynamic frequency selection, and adaptive modulation) to

obtain higher data throughput, they are still governed by a standard that limits

their full potential.



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By taking the CR approach into a challenging RF environment like the ISM

band, where inherently the devices need to accept any interference while

reducing the possibility of interfering with others, it provides a framework for us

to quantify current system performance and look at the improvements that

the CRs can provide. It is within bands that are heavily utilized that we see the

greater need for spectrum efficiency improvement and where the promises offered

by CR technology could render its greater benefits. Leveraging on the success of

wireless technologies such as 802.11 and new advances in emerging ones like

802.16 and .22 could help translate current CR research directly into commercial

benefits. This could enhance the possibility of the provision for commercial off-

the-shelf products for both military and public safety use.



10. Conclusion

Cognitive radio techniques offer a promising approach to dynamic

spectrum allocation. A simple example shows a 20 dB SINR improvement for

wireless LAN using cognitive techniques in an interference environment over that

provided by the current IEEE 802.11aservice PHY standard. Spectrum cognition

is essential to dynamic spectrum resource management at both node and

network level.

11. References

[1] “Cognitive Radio Ad Hoc Networks” 7 (2009) 810-836, Ian F. Akyildiz , Won-

Lee, Kaushik R. Chowdhury

[2] Akyildiz, I.F.; Won-Yeol Lee; Vuran, M.C.; Mohanty, S. “A Survey on Spectrum

Management in Cognitive Radio Networks” communications magazine, IEEE, vol.

46, Publication Year: 2008 , Page(s): 40 - 48

[3] Akyildiz, I.; Won-Yeol Lee; Chowdhury, K. , “Spectrum Management in

Cognitive Radio Ad Hoc Networks” Network ,IEEE, Volume: 23, Publication Year:

2009 , Page(s): 6 - 12

[4] Various web sites

cognitive radio networks (chapters)

Documents